



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		UNetaDModel
					A__					:		KarrasVeScheduler



					def __init__(  self       :							List[str]					,       _snake_case       :							UNetaDModel					,       _snake_case       :							KarrasVeScheduler       ):






								"""simple docstring"""


								super().__init__()
								self.register_modules(unet=_snake_case					,       scheduler=_snake_case       )



					@torch.no_grad()
					def __call__(  self       :							Optional[int]					,       _snake_case       :							int = 1					,       _snake_case       :							int = 50					,       _snake_case       :							Optional[Union[torch.Generator, List[torch.Generator]]] = None					,       _snake_case       :							Optional[str] = "pil"					,       _snake_case       :							bool = True					,       **_snake_case       :							int					,       ):






								"""simple docstring"""


								A__	  =     self.unet.config.sample_size
								A__	  =     (batch_size, 3, img_size, img_size)

								A__	  =     self.unet

								# sample x_0 ~ N(0, sigma_0^2 * I)
								A__	  =     randn_tensor(_snake_case					,       generator=_snake_case					,       device=self.device       ) * self.scheduler.init_noise_sigma

								self.scheduler.set_timesteps(_snake_case       )

								for t in self.progress_bar(self.scheduler.timesteps       ):
											# here sigma_t == t_i from the paper
											A__	  =     self.scheduler.schedule[t]
											A__	  =     self.scheduler.schedule[t - 1] if t > 0 else 0

											# 1. Select temporarily increased noise level sigma_hat
											# 2. Add new noise to move from sample_i to sample_hat
											A__  ,			A__	  =     self.scheduler.add_noise_to_input(_snake_case					,       _snake_case					,       generator=_snake_case       )

											# 3. Predict the noise residual given the noise magnitude `sigma_hat`
											# The model inputs and output are adjusted by following eq. (213) in [1].
											A__	  =     (sigma_hat / 2) * model((sample_hat + 1) / 2					,       sigma_hat / 2       ).sample

											# 4. Evaluate dx/dt at sigma_hat
											# 5. Take Euler step from sigma to sigma_prev
											A__	  =     self.scheduler.step(_snake_case					,       _snake_case					,       _snake_case					,       _snake_case       )

											if sigma_prev != 0:
														# 6. Apply 2nd order correction
														# The model inputs and output are adjusted by following eq. (213) in [1].
														A__	  =     (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2					,       sigma_prev / 2       ).sample
														A__	  =     self.scheduler.step_correct(
														    _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       step_output.prev_sample					,       step_output['derivative']					,       )
											A__	  =     step_output.prev_sample

								A__	  =     (sample / 2 + 0.5).clamp(0					,       1       )
								A__	  =     sample.cpu().permute(0					,       2					,       3					,       1       ).numpy()
								if output_type == "pil":
											A__	  =     self.numpy_to_pil(_snake_case       )

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=_snake_case       )





"""simple docstring"""



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    0
       __a       =    len(a				) - 1

       while left <= right:
              # avoid divided by 0 during interpolation
              if sorted_collection[left] == sorted_collection[right]:
                     if sorted_collection[left] == item:
                            return left
                     else:
                            return None

              __a       =    left + ((item - sorted_collection[left]) * (right - left)) // (
                  sorted_collection[right] - sorted_collection[left]
              )

              # out of range check
              if point < 0 or point >= len(a				):
                     return None

              __a       =    sorted_collection[point]
              if current_item == item:
                     return point
              else:
                     if point < left:
                            __a       =    left
                            __a       =    point
                     elif point > right:
                            __a       =    right
                            __a       =    point
                     else:
                            if item < current_item:
                                   __a       =    point - 1
                            else:
                                   __a       =    point + 1
       return None



def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a				):

       # avoid divided by 0 during interpolation
       if sorted_collection[left] == sorted_collection[right]:
              if sorted_collection[left] == item:
                     return left
              else:
                     return None

       __a       =    left + ((item - sorted_collection[left]) * (right - left)) // (
           sorted_collection[right] - sorted_collection[left]
       )

       # out of range check
       if point < 0 or point >= len(a				):
              return None

       if sorted_collection[point] == item:
              return point
       elif point < left:
              return interpolation_search_by_recursion(a   ,    a   ,    a   ,    a				)
       elif point > right:
              return interpolation_search_by_recursion(a   ,    a   ,    a   ,    a				)
       else:
              if sorted_collection[point] > item:
                     return interpolation_search_by_recursion(
                         a   ,    a   ,    a   ,    point - 1				)
              else:
                     return interpolation_search_by_recursion(
                         a   ,    a   ,    point + 1   ,    a				)



def 	_lowerCamelCase(	a				):
       if collection != sorted(a				):
              raise ValueError("Collection must be ascending sorted"				)
       return True


if __name__ == "__main__":
   import sys
   SCREAMING_SNAKE_CASE__:List[str]									=			0
   if debug == 1:
      SCREAMING_SNAKE_CASE__:Tuple									=			[10, 30, 40, 45, 50, 66, 77, 93]
      try:
         __assert_sorted(collection)
      except ValueError:
         sys.exit("""Sequence must be ascending sorted to apply interpolation search""")
      SCREAMING_SNAKE_CASE__:int									=			67

   SCREAMING_SNAKE_CASE__:List[str]									=			interpolation_search(collection, target)
   if result is not None:
      print(F'''{target} found at positions: {result}''')
   else:
      print("""Not found""")
